iCons2-MoS

Mapping Transportation Accessibility in Boston

Final Website: https://suobset.github.io/iCons/iCons2-MoS/index.html

Research Paper: https://github.com/suobset/iCons/blob/main/iCons2-MoS/assets/I2E%20-%20Final%20Report.pdf

Final Website Repository (code for website + links to final written report and video): https://github.com/suobset/iCons/tree/main/iCons2-MoS

Team Members

Kushagra Srivastava, Jack Minella, Gabrielle Walczak, Cleo Hein, and Yi Ding

Project Overview

The final product is an interactive website, which includes a "Blob map" will help users look through the accessibility for public transit at every location in Boston.

The data that have been collected are presented as a Voronoi Diagram overlaying every subway line of the MBTA. The Voronoi polygons on the map represents the blobs.

As the user hovers over a blob on the map on a computer or interactive digital display, an accessibility score between 0-100 will be displayed, to help the viewer judge the accessibility of a region. A high score means those living within the area have a greater opportunity to reduce their carbon footprint.

Based on the blob score, conclusions can be made regarding why some areas have higher scores than others. These blob scores may also reveal influential factors such as geography, community demographics, or other factors that alter transportation accessibility. Increasing the understanding of train accessibility throughout Boston will help the city achieve its ambitious carbon neutrality goal.

Data

The data used was made available by the Metropolitan Bay Transportation Authority via their V3 API.

Tools

MBTA subway station data in Boston was analyzed using R and displayed through a map made in GIS and Leaflet. The website with the map was made using HTML, CSS, javaScript.

Calculation

First, the area of the Voronoi polygon is calculated, each area will be normalized so that the polygon area values are on a scale of 0-100.

Then, the areas will be ordered from greatest to least in Excel, and they will be normalized using the formula

zi = 100 - (xi – min(x)) / (max(x) – min(x)) * 100

where zi is the i-th normalized value in the dataset, xi is the i-th value in the dataset, and min(x) and max(x) is the minimum and maximum value in the dataset, respectively.